Grid shape (curvilinear/structured versus triangular/unstructured) and grid size affect model output. In this study structured, unstructured and hybrid grids with a high and low resolution were compared. As a case study, we use the Waal River (with main channel and floodplains). We studied simulated water levels using the six grids, considering equal main channel friction, which enabled to study the isolated effects of grid shape and size. The spread in simulated water levels was found to be rather large with a maximum deviation of 78 cm. Therefore, calibration was performed such that simulated water levels resembled measured water levels by adjusting the main channel friction. This enabled us to draw conclusions on the choice of optimal usage of the grids in engineering studies. Bathymetry accuracy and numerical friction, both as a result of grid resolution, and numerical viscosity as a result of grid shape play a vital role. The analysis shows that unstructured grids are affected most by the calibration which is reflected in the wide spreading of calibrated friction values. From the six grids studied, the hybrid grid with curvilinear grid cells in the main channel and triangular grid cells in the floodplain is recommended for hydraulic modelling since computation time is low, while model output shows sufficient accuracy.

The uncertainty in flood frequency relations can be decreased by adding reconstructed historic flood events to the data set of measured annual maximum discharges. This study shows that an artificial neural network trained with a 1‐D/2‐D coupled hydraulic model is capable of reconstructing river floods with multiple dike breaches and inundations of the hinterland with high accuracy. The benefit of an artificial neural network is that it reduces computational times. With this network, the maximum discharge of the 1809 flood event of the Rhine River and its 95% confidence interval was reconstructed. The study shows that the trained artificial neural network is capable of reproducing the behavior of the hydraulic model correctly. The maximum discharge during the flood event was predicted with high accuracy even though the underlying input data are, due to the fact that the event occurred more than 200 years ago, uncertain. The confidence interval of the prediction was reduced by 43% compared to earlier predictions that did not use hydraulic models.

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas.

River dunes are wavelike features in sandy riverbeds that move slowly in a downstream direction. These bedforms are shaped by the interaction between flowing water and sand grains at the riverbed. Understanding river dune dynamics is essential to determine sediment transport and optimize river maintenance. Earlier research mainly focused on river dune dynamics during high flows; therefore, low‐flow dune dynamics are hardly understood. In this study, we analyzed dune dynamics in a unique data set of 10 years of biweekly bed elevation profiles of the Rhine River, focusing on low‐flow conditions. Here, we show that dunes migrate and become lower and longer during low flows, while current literature states that dunes should become shorter. This dune migration and lengthening at low flows is a new finding and is linked to diffusive dune migration processes that become more relevant when sediment transport is just above incipient motion.

Currently, the effect of dike breaches on downstream discharge partitioning and flood risk is not addressed in flood safety assessments. In a bifurcating river system, a dike breach may cause overland flows which can change downstream flood risk and discharge partitioning. This study examines how dike breaches and overflow affect overland flow patterns and discharges of the rivers of the Rhine delta. For extreme discharges, an increase in flood risk along the river branch with the smallest discharge capacity was found, while flood risk along the other river branches was reduced. Therefore, dike breaches and resulting overland flow patterns must be included in flood safety assessments.

Accurate models with low computational times are required to predict the consequences of fluvial floods in real‐time. Even though detailed hydraulic models can predict flood water levels and corresponding inundation extents with high accuracy, their computational times limit their applicability to being used as a flood early‐warning system. Therefore, conceptual models were developed in the literature for many years. These types of models do not attempt to represent the complex dynamic flood generation processes but are based on simplified hydraulic concepts, generally only using a digital elevation model as input. However, a shift in this research field is currently present from conceptual models to data‐driven models, and more specifically to neural networks. This paper discusses the benefits and drawbacks of both modelling approaches and speculates which method is most promising to be used as a flood warning system to predict the consequences of fluvial floods in real‐time.